{"id":"W2774307122","doi":"10.1038/nbt.4042","title":"Multiplexed droplet single-cell RNA-sequencing using natural genetic variation","year":2017,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1274,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Dental and Craniofacial Research; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Center for Chronic Disease Prevention and Health Promotion; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute","keywords":"Biology; Single-nucleotide polymorphism; Computational biology; RNA-Seq; RNA; Single-cell analysis; Genotyping; Transcriptome; Genetics; Gene; Genotype; Cell; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007701761,0.0004853702,0.0006926496,0.0002847326,0.0002721896,0.0008078618,0.0007941267,0.0005824019,0.001179994],"category_scores_gemma":[0.000773004,0.0004234169,0.0003712068,0.0003364426,0.0005420488,0.000612846,0.0006792519,0.0009562447,0.0006828663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008190868,"about_ca_system_score_gemma":0.000387792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005797976,"about_ca_topic_score_gemma":0.00224903,"domain_scores_codex":[0.9990621,0.000117657,0.00003863404,0.0004853533,0.0002471749,0.0000491743],"domain_scores_gemma":[0.9994591,0.0002348877,0.0000829792,0.0001200408,0.00006219989,0.0000409015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002552096,0.0000129494,0.0001728386,0.00002354141,0.000007775448,0.00001316015,0.00001912901,0.0004758325,0.9950554,0.0005799657,0.0001016034,0.00351222],"study_design_scores_gemma":[0.00001579906,0.00005450329,0.00118245,0.000003488412,0.0000109315,0.00007061294,0.00001586616,0.02360647,0.9713865,0.001029591,0.002600199,0.0000236886],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3002482,0.0008114947,0.6880332,0.0002581809,0.0002504915,0.0004304808,0.003141733,0.002778059,0.004048301],"genre_scores_gemma":[0.5591854,0.0005638385,0.4302943,0.0005093972,0.0000754596,0.0008574039,0.002258881,0.0004490289,0.005806223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001179994,"threshold_uncertainty_score":0.005942941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643778986858163,"score_gpt":0.2425866324279121,"score_spread":0.2261488425593305,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}